1. Field of the Invention
The present invention relates to a method of generating a detector for judging whether a predetermined image is a face image, which is suitable for detection of a face in a target image. The present invention also relates to a method, an apparatus, and a program for face detection using the detector.
2. Description of the Related Art
Correction of skin color in the face regions of people has been carried out in photographs obtained by digital cameras, based on the color distributions thereof. Recognition of a person is also carried out in a digital video image photographed by a digital camcorder of a monitoring system. In these cases, the face region corresponding to the face of the person needs to be detected in the digital image. Therefore, various methods have been proposed for face detection in a digital image. As a method of face detection achieving especially high detection accuracy and robustness has been known a method using a detector module (hereinafter simply referred to as a detector) generated according to a machine learning method using sample images (see “Fast Omni-Directional Face Detection”, Shihong LAO et al., Proceedings of Meeting on Image Detection and Understanding (MIRU) 2004, pp. II-271-II-276 and U.S. patent Application Publication No. 20050100195, for example).
In these methods, a detector is generated in advance which has learned characteristics of faces from a face sample image group comprising face sample images, in which the directions and the orientations of faces are substantially the same, and from a non-face sample image group comprising non-face images. The detector can judge whether an image represents a face in a predetermined direction and orientation. Partial images are sequentially cut from the image as targets of face detection (hereinafter referred to as detection target images), and whether the partial image is a face image is judged by use of the detector. In this manner, faces in the detection target images are detected. In order to maintain detection accuracy in a predetermined range, the learning by the detector is generally carried out based on the face sample images having the same face direction and orientation. In this case, the direction and orientation of a face that can be detected strongly depend on the face direction and orientation in the face sample images.
In the case where faces in arbitrary directions and orientations are to be detected in detection target images, a sample image group is generally generated for each combination of face directions and orientations, and the learning is carried out for each of the combinations based on the sample image groups. In this manner, a plurality of detectors are generated for the respective combinations, and the detectors are applied to a partial images cut sequentially from detection target images.
However, in the above manner, the sample image groups are necessary for the respective combinations of the face directions and orientations and the learning is carried out for the respective combinations based on the sample image groups. Consequently, preparation of the sample image groups and the learning are time-consuming and inefficient. For example, in the case where the face directions to be detected are front, right, and left and the orientations are 12 directions obtained by division of 360 degrees by 30 degrees, the number of the combinations is 36 (=3×12), for all of which the sample image groups and the learning are necessary.